Supporting Case-Based Reasoning with Neural Networks: An Illustration for Case Adaptation

Abstract: Case-based reasoning (CBR) is a knowledge-based reasoning and learning methodology that applies prior cases --- records of prior instances or experiences --- by adapting their lessons to solve new problems. The CBR process enables explainable reasoning from few examples, with minimal learning cost. However, the success of CBR depends on having appropriate similarity and adaptation knowledge, which may be hard to acquire. This paper illustrates the opportunity to leverage neural network methods to reduce the knowledge engineering burden for case-based reasoning. It presents an experimental example from ongoing work on refining the case difference heuristic approach to learning case adaptation knowledge by applying neural network learning.